An analysis technique, so-called Synchronous Time Average (STA) method is commonly used in the process of monitoring the condition and operation of rotating objects. These are typically rotating shafts of various machines, or other rotating parts, such as cogged wheels of a gearbox, or turbine rotors, as well as different kinds of objects running in devices. Objects to be monitored include for example rolls, pumps, blowers, screens, grinders, barking drums and mixers as well as wires and felts in the manufacturing process of paper and pulp. All the above-mentioned objects move periodically. Typically the objects are subjected to friction causing wearing and faults that can break the machine or the object that is being monitored. Vibration sensors attached to the object being monitored are typically used for the measurements, said sensors measuring the vibration as acceleration, speed, or displacement. Pressure sensors are also used in monitoring pumps, screens and hydraulics, said pressure sensors being used for measuring pressure pulsations occurring in pipeworks.
In the STA method the signal to be measured is divided into time periods corresponding the rotating cycles, said time periods being averaged with respect to each other. The averaged periods are typically determined from a synchronizing signal obtained by measuring the rotating frequency of the object by means of a separate synchronizing sensor. It is, for example, possible to install a magnet on the periphery of the rotating axis of the rotating object, wherein the magnetic synchronizing sensor gives one pulse per cycle, when the magnet passes by said sensor. The essential aspect in the STA method is that the synchronizing pulse is always obtained from the same point of the rotating cycle. Thus, all such variations, which in the actual measurement signal occur at the rotating frequency or its multiple, are of the same phase in each sample to be averaged. Correspondingly, the phase of the signal components occurring at other frequencies varies from one sample to another. In the averaging process the signal components whose phase is the same in all samples, remain in the average value. The components whose phase is not synchronized with the rotating frequency disappear, because their phase varies from one sample to another in the averaging process. Thus, the STA method is also one kind of a noise removal method, by means of which it is possible to remove all such frequency components from the measurement signal, which are not synchronized with the rotation and the rotating frequency of the rotating object to be measured. If there are periodical variations caused by several different objects, it is possible to use the STA method to distinguish the variation caused by each rotating object separately. Furthermore, the noise-type random variations or measurement noise summing to the measurement signal can be eliminated by means of the STA method.
FIGS. 1a to 1d illustrate the application of the STA method when measuring damage on the surface of a rotating roll. In the example of FIG. 1a it is determined to which extent a press roll 1 causes vibrations that are produced for example by a shape error 3 in the press roll. The roll 2 also contains shape errors 4, but the aim is to filter off their effect and the vibration caused by the measurement, as well as vibrations of other sources. The vibrations are periodical variations, because they are repeated at regular intervals that depend on the speed of rotation. The filtering takes place by averaging several measurements, each of them being started at the same point (origin of the time axis) at the rotation of the roll 1. Each measurement is started by a synchronizing pulse or signal 9 obtained from a sensor 7 that is for example a magnetic sensor or an optical sensor, depending on the object to be measured. The sensor 7 detects a magnet 5 passing by, said magnet being attached for example on the shaft of the roll 1.
The first measurement is shown in FIG. 1a, in which a vibration 11 illustrates a change caused by a shape error 3 in the signal given by the acceleration sensor 6. The acceleration sensor 6 is attached to the bearing housing of the roll 1 that is being analysed. The information of the sensors is transferred to the analysing apparatus 10. The analysing apparatus 10 collects information, analyses it and performs the necessary calculation and reporting. It is typically a computer in which the measurement data is entered.
A second measurement is shown in FIG. 1b, which illustrates the fact that the vibration 11 does not shift on the time axis, but the vibration 12 caused by the shape error 4 of the roll, has, in turn, shifted on the time axis. This results from the fact that the diameters of the rolls deviate from each other and each measurement begins at the moment where the position of the shape error 4 varies. However, the sensor 5 and the shape error 3 are always in the same position during the measurement.
A third measurement is shown in FIG. 1c, and the vibration 12 has again changed its position on the time axis. The vibration 11 synchronized with the roll 1, is precisely in the same location in each measurement. There are for example several hundreds of measuring cycles, corresponding to the hundreds of revolutions of the roll 1. When the results of all the measurements are summed up and divided with the number of measurements, a synchronized time averaged signal shown in FIG. 1d is obtained as a result. However, vibration not synchronized with the roll 1 approaches zero. The larger the number of average values obtained, the closer to zero the portion of the unsynchronized vibration comes, and this results in an improved signal-to-noise ratio for the part of the averaged signal. As noise it is possible to consider all the parts of the signal that are not synchronized with the triggering target, for example the roll 1.
In order to be able to function, the above-described method requires a separate synchronizing signal by means of which the rotating frequency and phase are monitored, and in order to apply the method, it is necessary to install a separate synchronizing sensor for the object to be monitored. This is naturally an expense that can be eliminated, if the averaging can be made without a separate synchronizing signal. Often there occurs a situation that the information on the speed of rotation of the object can be obtained for example from the control system of electric drives that measures speed by means of a tachometer. Even if a separate synchronizing signal would not be obtained from the tachometer, it is possible that the analysis can be carried out on the basis of the information on the speed of rotation only. Thus the measurement signal to be averaged is divided into sequences whose length corresponds to the time of the rotating cycle calculated by means of the speed of rotation. In an ideal case, the exact same result is obtained in this manner than when using a separate synchronizing signal. If the object rotates at constant speed, one pulse per revolution is obtained from the synchronizing sensor in such a manner that the pulse interval remains constant. Correspondingly, the same pulse interval can be calculated without the synchronizing sensor, if absolutely correct information on the rotating frequency is available. The only information lost when the pulse sensor is not taken in use is the absolute phase information, in other words the starting point of the sequences to be averaged is positioned in a random location with respect to the rotating cycle of the machine.
The above-described method is disclosed for example in the document U.S. Pat. No. 6,789,025 B2, in which the method is called the cyclic time average (CTA) method.
However, in connection with the present invention it has been noted that there are many elements of uncertainty in the act of applying the CTA method and other corresponding methods for monitoring the condition and operation of rotating objects, wherein the results of the analysis may be unreliable. The most significant risk relates to the fact that the methods are very sensitive even to small errors in the rotating speed information.
The effect of the error can be illustrated by means of the following simulated example, in which it is assumed that the object rotates at a rotating frequency of 10.0 Hz. The measurement signal consist of the basic frequency (1*RPM) at the rotating speed and two of its harmonic multiples (3rd multiple: 3*RPM and 8th multiple 8*RPM). The signal of the object having the length of one rotating cycle (0.1 s) is shown in the circular set of coordinates in FIG. 2a. In the simulation white noise and other sequential signal components are summed up to the above-mentioned sequential signal. The power spectrum of the sum signal is shown in FIG. 2c and the time level sample of one second is shown in FIG. 2b. These describe by means of an example a measurement signal obtained of an object from the vibration sensor.
By means of the CTA method it is now possible to attempt to separate a signal component synchronizing with the rotating frequency of 10.0 Hz, wherein the result should comply with FIG. 2a. According to the method, the measurement signal is divided into consecutive sequences corresponding to the cycle time of the rotation, said sequences being averaged with each other. The result of averaging hundred cycles is shown in FIG. 2d simultaneously with the curve shown in FIG. 2a. As can be seen in FIG. 2d, the wave-form of the variations searched by means of the method is repeated and the effect of the noise and other harmonic components remains insignificant.
At the next stage the averaging is conducted as shown hereinabove in accordance with FIGS. 2a to 2d so that it is assumed that there are errors of different magnitude in the rotating speed information. In the situation of FIG. 2e, there is an error of 0.005 Hz in the rotating speed information, in FIG. 2f an error of 0.010 Hz, in FIG. 2g an error of 0.020 Hz and in FIG. 2h an error of 0.030 Hz. As FIGS. 2e to 2h show, even quite small errors in the speed of rotation cause a significant error in the average value. The reason for this is that the signal component to be searched is no longer summed at the same phase in the averaging, in other words each measurement starts for example in a different position of the rotating shaft, because the actual speed does not correspond to the measurement information obtained from the sensor or the control system. For example an error of two per mille (0.020 Hz) in the rotating speed information causes a phase shift of approximately 0.7 degrees in the rotating frequency component between each two successive sequences to be averaged. Thus, for example between the 1st and the 100th sequence there exists a phase difference of 70 degrees. For the higher multiples of the rotating frequency the error has an even stronger effect because the error in the length of the averaging cycle in relation to the cycle time of the frequency component is the larger the higher the frequency in question.
In addition to the uncertainty of the rotating speed information it should be noted that quite often the rotating speed varies during the measurement, wherein the length of the individual cycles to be averaged on the time level is not an invariable constant with respect to time. In the conventional STA method this is not disadvantageous because the length of each rotating cycle is measured separately with a synchronization pulse and the cycles of the measurement to be averaged always begin at the same point with respect to the rotating cycle, irrespective of the speed variation. In the above-presented cyclic time average method (CTA), it is not possible to monitor the variation in the length of the rotating cycle during the measurement.
Document FI 91919 B discloses a method in which the cyclic time average is calculated without the synchronization pulse. In the method the aim is to determine the periodical variations in the quality variables of paper and to recognize the machine means causing the variation. The averaging is performed as described above, but in this case several test frequencies are used for calculating the average value, said frequencies being located within the environment of the expected rotating frequency. In principle, results corresponding to FIGS. 2e to 2h are calculated, and the best one of them when compared to certain criteria is selected, said best result thus corresponding to the actual speed of rotation. Thus, by means of the method it is possible to calculate several results in the vicinity of a certain rotating frequency, by the same principle as presented in FIG. 2e to 2h, and to select the frequency that produces the best result on the basis of the set criteria. The method could in some cases solve the problem of the previous example that results from the inaccuracy in the rotating speed information. However, it would then be necessary to calculate the same analysis with quite a large group of different rotating frequencies, because for example in the case discussed hereinabove, the entire area to be examined should be covered in the environment of 10 Hz with a resolution of approximately 0.001 Hz. The analysis and comparison of the results also requires a great deal of work. Furthermore, the changes in the speed of rotation during the measurement still remain an unsolved problem.